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Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces
- Bioinformatics
, 2006
"... MOTIVATION: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding “hot-spots”, and rank them according to sequence conservation and simple meas ..."
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Cited by 45 (0 self)
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MOTIVATION: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding “hot-spots”, and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatics and van der Waals energies, to predict which are involved in binding in the native complex. RESULTS: The resulting differences between predicting binding-sites at protein-protein and protein-ligand interfaces are striking. There is a high level of prediction accuracy (C93%) for protein-ligand interactions, based on the following attributes: van der Waals potential, electrostatic potential, desolvation and surface conservation. Generally, the prediction accuracy for protein-protein interactions is lower, with the exception of enzymes. Our results show that the ease of cleft desolvation or “de-wetting ” is strongly predictive of interfaces and strongly maintained across all classes of protein binding interface.
Recent progress and future directions in protein-protein docking
"... This article gives an overview of recent progress in protein-protein docking and it identifies several directions for future research. Recent results from the CAPRI blind docking experiments show that docking algorithms are steadily improving in both reliability and accuracy. Current docking algorit ..."
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Cited by 37 (5 self)
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This article gives an overview of recent progress in protein-protein docking and it identifies several directions for future research. Recent results from the CAPRI blind docking experiments show that docking algorithms are steadily improving in both reliability and accuracy. Current docking algorithms employ a range of efficient search and scoring strategies, including e.g. fast Fourier transform correlations, geometric hashing, and Monte Carlo techniques. These approaches can often produce a relatively small list of up to a few thousand orientations, amongst which a near-native binding mode is often observed. However, despite the use of improved scoring functions which typically include models of desolvation, hydrophobicity, and electrostatics, current algorithms still have difficulty in identifying the correct solution from the list of false positives, or decoys. Nonetheless, significant progress is being made through better use of bioinformatics, biochemical, and biophysical information such as e.g. sequence conservation analysis, protein interaction databases, alanine scanning, and NMR residual dipolar coupling restraints to help identify key binding residues. Promising new approaches to incorporate models of protein flexibility during docking are being developed, including the use of molecular dynamics snapshots, rotameric and off-rotamer searches, internal coordinate mechanics, and principal component analysis based techniques. Some investigators now use explicit solvent models in their docking protocols. Many of these approaches
Prediction of dna-binding residues from sequence
- Bioinformatics
"... Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identifica-tion of binding sites requires expensive and laborious methods such as mutagenesis and binding essa ..."
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Cited by 31 (0 self)
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Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identifica-tion of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available. Results: It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these char-acteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein–DNA complexes available today, we found that 89 % of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information.
Optimal clustering for detecting near-native conformations in protein docking
- Biophys. J
"... ABSTRACT Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because longrange electrostatic and/or desolvation forces stee ..."
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Cited by 11 (6 self)
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ABSTRACT Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because longrange electrostatic and/or desolvation forces steer the proteins to a low free-energy attractor at the binding region. Something similar occurs in the docking of small molecules, although in this case shorter-range van der Waals forces play a more critical role. Based on the above, we have developed two different clustering strategies to predict docked conformations based on the clustering properties of a uniform sampling of low free-energy protein-protein and protein-small molecule complexes. We report on significant improvements in the automated prediction and discrimination of docked conformations by using the cluster size and consensus as a ranking criterion. We show that the success of clustering depends on identifying the appropriate clustering radius of the system. The clustering radius for protein-protein complexes is consistent with the range of the electrostatics and desolvation free energies (i.e., between 4 and 9 A ˚); for protein-small molecule docking, the radius is set by van der Waals interactions (i.e., at;2 A ˚). Without any a priori information, a simple analysis of the histogram of distance separations between the set of docked conformations can evaluate the clustering properties of the data set. Clustering is observed when the histogram is bimodal. Data clustering is optimal if one chooses the clustering radius to be the minimum after the first peak of the bimodal distribution. We show that using this optimal radius further improves the discrimination of near-native complex structures.
Solvated docking: introducing water into the modelling of biolmolecular complexes
- Bioinformatics
"... www.nmr.chem.uu.nl ..."
Haddock(2p2i): A biophysical model for predicting the binding affinity of protein-protein interaction inhibitors
- J. Chem. Inf. Model
"... ABSTRACT: The HADDOCK score, a scoring function for both protein−protein and protein-nucleic acid modeling, has been successful in selecting near-native docking poses in a variety of cases, including those of the CAPRI blind prediction experiment. However, it has yet to be optimized for small molecu ..."
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Cited by 2 (0 self)
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ABSTRACT: The HADDOCK score, a scoring function for both protein−protein and protein-nucleic acid modeling, has been successful in selecting near-native docking poses in a variety of cases, including those of the CAPRI blind prediction experiment. However, it has yet to be optimized for small molecules, and in particular inhibitors of protein− protein interactions, that constitute an “unmined gold reserve ” for drug design ventures. We describe here HADDOCK2P2I, a biophysical model capable of predicting the binding affinity of protein−protein complex inhibitors close to experimental error (∼2-fold larger). The algorithm was trained and 4-fold cross-validated against experimental data for 27 inhibitors targeting 7 protein−protein complexes of various functions and tested on an independent set of 24 different inhibitors for which Kd/IC50 data are available. In addition, two popular ligand topology generation and parametrization methods (ACPYPE and PRODRG) were assessed. The resulting HADDOCK2P2I model, derived from the original HADDOCK score, provides insights into inhibition determinants: while the role of electrostatics and desolvation energies is case-dependent, the interface area plays a more critical role compared to protein−protein interactions.
Structural Bioinformatics FRODOCK: a new approach for fast rotational protein-protein docking
"... Motivation: Prediction of protein-protein complexes from the coor-dinates of their unbound components usually starts by generating many potential predictions from a rigid-body six-dimensional search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new ..."
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Motivation: Prediction of protein-protein complexes from the coor-dinates of their unbound components usually starts by generating many potential predictions from a rigid-body six-dimensional search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard pro-tein-protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving su-perior efficiency. Moreover, a parallel version of the method per-forms the docking search in just a few minutes, openning new appli-cation opportunities in the current “omics ” world.
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"... cules remains a major challenge (Morelli et al., 2011; Villoutreix different challenges. For example, inhibitors required to mimicthe molecular basis for the reduced druggability of PPIs, in terms of howprotein surfaces interactwith smallmolecules. To focuson current approaches, we have chosen to ci ..."
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cules remains a major challenge (Morelli et al., 2011; Villoutreix different challenges. For example, inhibitors required to mimicthe molecular basis for the reduced druggability of PPIs, in terms of howprotein surfaces interactwith smallmolecules. To focuson current approaches, we have chosen to cite recent applications been great interest in the use of biologics to target PPIs. It is our opinion that, in the majority of cases, extracellular targets are best approached with biologics such as antibodies or proteinof each approach rather than earlier work in their development. Although most approved PPI inhibitors currently find applica-tion as treatments for cancer or in regulation of the immune sys-drugs. In contrast, biologics are inherently less suitable for intra-cellular targets in the current state of the art, necessitating the use of small molecules. While the use of biologics to target PPIset al., 2014; Zinzalla and Thurston, 2009). In this review, we detail the specific chemical and biological challenges associated with inhibiting PPIs using small molecules, as well as the competitive advantages. We then discuss novel experimental and computa-tional approaches to developing PPI inhibitors, with illustrative
BIOINFORMATICS REVIEW doi:10.1093/bioinformatics/btm323 Structural bioinformatics Interaction-site prediction for protein complexes: a critical
, 2007
"... Motivation: Proteins function through interactions with other proteins and biomolecules. Protein–protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface ..."
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Motivation: Proteins function through interactions with other proteins and biomolecules. Protein–protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of its applications and project future developments is in order. Summary: Interface prediction methods rely on a wide range of sequence, structural and physical attributes that distinguish interface residues from non-interface surface residues. The input data are manipulated into either a numerical value or a probability representing the potential for a residue to be inside a protein interface. Predictions are now satisfactory for complex-forming proteins that are well represented in the Protein Data Bank, but less so for underrepresented ones. Future developments will be directed at tackling problems such as building structural models for multi-component structural complexes. Contact:
